Instructions to use MengqiLei/hyper-align with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MengqiLei/hyper-align with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MengqiLei/hyper-align")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MengqiLei/hyper-align", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MengqiLei/hyper-align with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MengqiLei/hyper-align" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MengqiLei/hyper-align", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MengqiLei/hyper-align
- SGLang
How to use MengqiLei/hyper-align with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "MengqiLei/hyper-align" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MengqiLei/hyper-align", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "MengqiLei/hyper-align" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MengqiLei/hyper-align", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MengqiLei/hyper-align with Docker Model Runner:
docker model run hf.co/MengqiLei/hyper-align
Hyper-Align
This repository contains the released Hyper-Align projector checkpoint for the paper Hypergraph as Language.
Hyper-Align is a hypergraph-native alignment framework that makes high-order association structures directly consumable by a frozen large language model. This checkpoint uses the HIDT-O hypergraph serialization protocol and the HIP projector to map hypergraph incidence information into the token space of Qwen/Qwen3-8B.
This repository does not include the base LLM or text encoder weights. Users must download those models from their official Hugging Face repositories.
Files
config.json
mm_projector.bin
config.jsondefines the Hyper-Align wrapper and HIP projector configuration.mm_projector.bincontains the trained projector weights.
Keep both files in the same checkpoint directory. The evaluation code uses config.json to instantiate the Hyper-Align model wrapper and projector before loading mm_projector.bin.
License
The Hyper-Align code and released projector checkpoint are distributed under the Apache License 2.0.
The base LLM and embedding model are not redistributed here. Users must comply with the licenses and terms of the corresponding upstream model repositories:
Qwen/Qwen3-8BQwen/Qwen3-Embedding-0.6B
Citation
If you use this checkpoint, please cite:
@article{lei2026hypergraph,
title={Hypergraph as Language},
author={Lei, Mengqi and Xie, Guohuan and Ying, Shihui and Du, Shaoyi and Yong, Jun-Hai and Li, Siqi and Gao, Yue},
journal={arXiv preprint arXiv:2605.21858},
year={2026}
}
Links
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